Reinforcement Learning Based Energy Management Algorithm for Energy Trading and Contingency Reserve Application in a Microgrid

被引：0

作者：

Hau, ChongAih ^{[1
]}

Radhakrishnan, Krishnanand Kaippilly ^{[2
]}

Siu, JunYen ^{[1
]}

Panda, Sanjib Kumar ^{[1
]}

机构：

[1] Natl Univ Singapore, Elect & Comp Engn Dept, Singapore, Singapore

[2] Berkeley Educ Alliance Res Singapore, SinBerBEST, Singapore, Singapore

来源：

2020 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT-EUROPE 2020): SMART GRIDS: KEY ENABLERS OF A GREEN POWER SYSTEM | 2020年

基金：

新加坡国家研究基金会;

关键词：

Microgrid; energy management; dynamic pricing; contingency reserve; model-free reinforcement learning;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents automation of energy trading using a value-based reinforcement learning and Deep-Q-Network based energy management algorithm for microgrid participants having energy storage systems, while maintaining required contingency reserves for the microgrid. A piecewise utility function is designed to form the trading strategies in response to the dynamic environment. By adjusting parameters of the utility function, two different behaviors of the microgrid operator, namely risk-seeking and risk-averse, are studied to analyze the impact on the energy storage's operation decision. The simulation results show that the proposed algorithm manages the storage optimally and outperforms rule-based algorithm, providing higher monetary benefits and better flexibility during an extreme scenario having highly dynamic pricing.

引用

页码：1005 / 1009

页数：5

共 10 条

[1]

[Anonymous], 2018, ABS180106285 CORR

[2] Local Energy Trending Behavior Modeling With Deep Reinforcement Learning [J].

Chen, Tao ;

Su, Wencong .

IEEE ACCESS, 2018, 6 :62806-62814

[3] Reinforcement Learning Approach for Optimal Distributed Energy Management in a Microgrid [J].

Foruzan, Elham ;

Soh, Leen-Kiat ;

Asgarpoor, Sohrab .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (05) :5749-5758

[4]

Guan CX, 2015, CONSUM COMM NETWORK, P637, DOI 10.1109/CCNC.2015.7158054

[5] Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond [J].

Hong, Tao ;

Pinson, Pierre ;

Fan, Shu ;

Zareipour, Hamidreza ;

Troccoli, Alberto ;

Hyndman, Rob J. .

INTERNATIONAL JOURNAL OF FORECASTING, 2016, 32 (03) :896-913

[6] Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings [J].

Kim, Sunyong ;

Lim, Hyuk .

ENERGIES, 2018, 11 (08)

[7]

Mnih V., 2013, PLAYING ATARI DEEP R

[8] Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach [J].

Rahbar, Katayoun ;

Xu, Jie ;

Zhang, Rui .

IEEE TRANSACTIONS ON SMART GRID, 2015, 6 (01) :124-134

[9]

Schaul T., 2015, INT C LEARN REPR ICL

[10] Artificial Intelligence Based Smart Energy Community Management: A Reinforcement Learning Approach [J].

Zhou, Suyang ;

Hu, Zijian ;

Gu, Wei ;

Jiang, Meng ;

Zhang, Xiao-Ping .

CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2019, 5 (01) :1-10

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